Path-based navigation of tubular networks

ABSTRACT

Provided are systems and methods for path-based navigation of tubular networks. In one aspect, the method includes receiving location data from at least one of a set of location sensors and a set of robot command inputs, the location data being indicative of a location of an instrument configured to be driven through a luminal network. The method also includes determining a first estimate of the location of the instrument at a first time based on the location data, determining a second estimate of the location of the instrument at the first time based on the path, and determining the location of the instrument at the first time based on the first estimate and the second estimate.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/678,970, filed May 31, 2018, which is hereby incorporated byreference in its entirety.

TECHNICAL FIELD

The systems and methods disclosed herein are directed to surgicalrobotics, and more particularly to navigation of a medical instrumentwithin a tubular network of a patient's body based at least in part on apath.

BACKGROUND

Bronchoscopy is a medical procedure that allows a physician to examinethe inside conditions of a patient's lung airways, such as bronchi andbronchioles. The lung airways carry air from the trachea, or windpipe,to the lungs. During the medical procedure, a thin, flexible tubulartool, known as a bronchoscope, may be inserted into the patient's mouthand passed down the patient's throat into his/her lung airways, andpatients are generally anesthetized in order to relax their throats andlung cavities for surgical examinations and operations during themedical procedure.

In the related art, a bronchoscope can include a light source and asmall camera that allows a physician to inspect a patient's windpipe andairways, and a rigid tube may be used in conjunction with thebronchoscope for surgical purposes, e.g., when there is a significantamount of bleeding in the lungs of the patient or when a large objectobstructs the throat of the patient. When the rigid tube is used, thepatient is often anesthetized. Coincident with the rise of otheradvanced medical devices, the use of robotic bronchoscopes areincreasingly becoming a reality. Robotic bronchoscopes providetremendous advantages in navigation through tubular networks. They areeasy to use and allow therapy and biopsies to be administeredconveniently even during the bronchoscopy stage.

Apart from mechanical devices or platforms, e.g., robotic bronchoscopesdescribed above, various methods and software models may be used to helpwith the surgical operations. As an example, a computerized tomography(CT) scan of the patient's lungs is often performed during pre-operationof a surgical examination. Data from the CT scan may be used to generatea three dimensional (3D) model of airways of the patient's lungs, andthe generated 3D model enables a physician to access a visual referencethat may be useful during the operative procedure of the surgicalexamination.

However, previous techniques for navigation of tubular networks stillhave challenges, even when employing medical devices (e.g., roboticbronchoscopes) and when using existing methods (e.g., performing CTscans and generating 3D models). As one example, motion estimation of amedical device (e.g., a bronchoscope tool) inside a patient's body maynot be accurate based on location and orientation change of the device,and as a result the device's position may not be accurately or correctlylocalized inside the patient's body in real time. Inaccurate locationinformation for such an instrument may provide misleading information tothe physician that uses the 3D model as a visual reference duringmedical operation procedures.

Thus, there is a need for improved techniques for navigating through anetwork of tubular structures.

SUMMARY

The systems, methods and devices of this disclosure each have severalinnovative aspects, no single one of which is solely responsible for thedesirable attributes disclosed herein.

In one aspect, there is provided a medical robotic system, comprising aset of one or more processors; and at least one computer-readable memoryin communication with the set of processors and having stored thereon amodel of a luminal network of a patient, a position of a target withrespect to the model, and a path along at least a portion of the modelfrom an access point to the target, the memory further having storedthereon computer-executable instructions to cause the set of processorsto: receive location data from at least one of a set of location sensorsand a set of robot command inputs, the location data being indicative ofa location of an instrument configured to be driven through the luminalnetwork, determine a first estimate of the location of the instrument ata first time based on the location data, determine a second estimate ofthe location of the instrument at the first time based on the path, anddetermine the location of the instrument at the first time based on thefirst estimate and the second estimate.

In another aspect, there is provided a non-transitory computer readablestorage medium having stored thereon instructions that, when executed,cause at least one computing device to: receive location data from atleast one of a set of location sensors and a set of robot commandinputs, the location data being indicative of a location of aninstrument configured to be driven through a luminal network of apatient; determine a first estimate of the location of the instrument ata first time based on the location data; determine a second estimate ofthe location of the instrument at the first time based on a path storedon at least one computer-readable memory, the non-transitory computerreadable storage medium further having stored thereon a model of theluminal network, a position of a target with respect to the model, andthe path, the path defined along at least a portion of the model from anaccess point to the target, and determine the location of the instrumentat the first time based on the first estimate and the second estimate.

In yet another aspect, there is provided a method of estimating alocation of an instrument, comprising: receiving location data from atleast one of a set of location sensors and a set of robot commandinputs, the location data being indicative of a location of aninstrument configured to be driven through a luminal network of apatient; determining a first estimate of the location of the instrumentat a first time based on the location data; determining a secondestimate of the location of the instrument at the first time based on apath stored on at least one computer-readable memory, at least onecomputer-readable memory having stored thereon a model of the luminalnetwork, a position of a target with respect to the model, and the path,the path defined along at least a portion of the model from an accesspoint to the target, and determining the location of the instrument atthe first time based on the first estimate and the second estimate.

In still yet another aspect, there is provided a medical robotic system,comprising a set of one or more processors; and at least onecomputer-readable memory in communication with the set of processors andhaving stored thereon a model of a mapped portion of a luminal networkof a patient, a position of a target with respect to the model, and apath along at least a portion of the model from an access point to thetarget, the memory further having stored thereon computer-executableinstructions to cause the set of processors to: determine that the pathleaves the mapped portion of the luminal network before reaching thetarget, display a current location of an instrument via at least a firstmodality, the first modality derives a location based on location datareceived from a set of one or more location sensors and the mappedportion of the model, the instrument is configured to be driven throughthe luminal network, determine, based on the current location, that thedistal end of the instrument is within a threshold range of a point atwhich the path leaves the mapped portion of the luminal network, and inresponse to determining that that the distal end of the instrument iswithin the threshold range of the point, update the current location ofthe instrument based on a reduction of a weight given to the firstmodality.

In yet another aspect, there is provided non-transitory computerreadable storage medium having stored thereon instructions that, whenexecuted, cause at least one computing device to: determine that a pathleaves a mapped portion of a luminal network of a patient beforereaching a target, at least one computer-readable memory having storedthereon a model of the mapped portion of the luminal network, a positionof the target with respect to the model, and the path along at least aportion of the model from an access point to the target; display acurrent location of an instrument via at least a first modality, thefirst modality derives a location based on location data received from aset of one or more location sensors and the mapped portion of the model,the instrument is configured to be driven through the luminal network;determine, based on the current location, that the distal end of theinstrument is within a threshold range of a point at which the pathleaves the mapped portion of the luminal network; and in response todetermining that that the distal end of the instrument is within thethreshold range of the point, update the current location of theinstrument based on a reduction of a weight given to the first modality.

In another aspect, there is provided a method of determining a locationof an instrument, comprising: determining that a path leaves a mappedportion of a luminal network of a patient before reaching a target, atleast one computer-readable memory having stored thereon a model of themapped portion of the luminal network, a position of the target withrespect to the model, and the path along at least a portion of the modelfrom an access point to the target; displaying a current location of aninstrument via at least a first modality, the first modality derives alocation based on location data received from a set of one or morelocation sensors and the mapped portion of the model, the instrument isconfigured to be driven through the luminal network; determining, basedon the current location, that the distal end of the instrument is withina threshold range of a point at which the path leaves the mapped portionof the luminal network; and in response to determining that that thedistal end of the instrument is within the threshold range of the point,updating the current location of the instrument based on a reduction ofa weight given to the first modality.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed aspects will hereinafter be described in conjunction withthe appended drawings, provided to illustrate and not to limit thedisclosed aspects, wherein like designations denote like elements.

FIG. 1A shows an example surgical robotic system, according to oneembodiment.

FIGS. 1B-1F show various perspective views of a robotic platform coupledto the surgical robotic system shown in FIG. 1A, according to oneembodiment.

FIG. 2 shows an example command console for the example surgical roboticsystem, according to one embodiment.

FIG. 3A shows an isometric view of an example independent drivemechanism of the instrument device manipulator (IDM) shown in FIG. 1A,according to one embodiment.

FIG. 3B shows a conceptual diagram that shows how forces may be measuredby a strain gauge of the independent drive mechanism shown in FIG. 3A,according to one embodiment.

FIG. 4A shows a top view of an example endoscope, according to oneembodiment.

FIG. 4B shows an example endoscope tip of the endoscope shown in FIG.4A, according to one embodiment.

FIG. 5 shows an example schematic setup of an EM tracking systemincluded in a surgical robotic system, according to one embodiment.

FIGS. 6A-6B show an example anatomical lumen and an example 3D model ofthe anatomical lumen, according to one embodiment.

FIG. 7 shows a computer-generated 3D model representing an anatomicalspace, according to one embodiment.

FIGS. 8A-8D show example graphs illustrating on-the-fly registration ofan EM system to a 3D model of a path through a tubular network,according to one embodiment.

FIGS. 8E-8F show effect of an example registration of the EM system to a3D model of a branched tubular network, according to one embodiment.

FIG. 9A shows a high-level overview of an example block diagram of anavigation configuration system, according to one embodiment.

FIG. 9B shows an example block diagram of the estimated state data storeincluded in the state estimator, according to one embodiment.

FIG. 10 shows an example block diagram of the path-based algorithmmodule in accordance with aspects of this disclosure.

FIG. 11 is a flowchart illustrating an example method operable by arobotic system, or component(s) thereof, for path-based navigation oftubular networks in accordance with aspects of this disclosure.

FIG. 12 is a simplified example model of a portion of a luminal networkfor describing aspects of this disclosure related to path-based locationestimation.

FIG. 13 is an example view of a model overlaid on a luminal network inaccordance with aspects of this disclosure.

FIG. 14 is a flowchart illustrating another example method operable by arobotic system, or component(s) thereof, for path-based navigation oftubular networks in accordance with aspects of this disclosure.

FIG. 15 illustrates a portion of the luminal network of FIG. 13including a mapped portion and an unmapped portion in accordance withaspects of this disclosure.

FIG. 16 is a view of a 3D model including tracked locations of a distalend of an instrument in accordance with aspects of this disclosure.

FIG. 17 show an example pre-operative method for preparation of asurgical instrument (e.g., an instrument tip) to navigate through anexample tubular network, according to various embodiments.

Reference will now be made in detail to several embodiments, examples ofwhich are illustrated in the accompanying figures. It is noted thatwherever practicable similar or like reference numbers may be used inthe figures and may indicate similar or like functionality. The figuresdepict embodiments of the described system (or method) for purposes ofillustration only. One skilled in the art will readily recognize fromthe following description that alternative embodiments of the structuresand methods illustrated herein may be employed without departing fromthe principles described herein.

DETAILED DESCRIPTION I. Surgical Robotic System

FIG. 1A shows an example surgical robotic system 100, according to oneembodiment. The surgical robotic system 100 includes a base 101 coupledto one or more robotic arms, e.g., robotic arm 102. The base 101 iscommunicatively coupled to a command console, which is further describedwith reference to FIG. 2 in Section II. Command Console. The base 101can be positioned such that the robotic arm 102 has access to perform asurgical procedure on a patient, while a user such as a physician maycontrol the surgical robotic system 100 from the comfort of the commandconsole. In some embodiments, the base 101 may be coupled to a surgicaloperating table or bed for supporting the patient. Though not shown inFIG. 1 for purposes of clarity, the base 101 may include subsystems suchas control electronics, pneumatics, power sources, optical sources, andthe like. The robotic arm 102 includes multiple arm segments 110 coupledat joints 111, which provides the robotic arm 102 multiple degrees offreedom, e.g., seven degrees of freedom corresponding to seven armsegments. The base 101 may contain a source of power 112, pneumaticpressure 113, and control and sensor electronics 114—includingcomponents such as a central processing unit (e.g., one or moreprocessors) 121, data bus, control circuitry, and memory (e.g., acomputer-readable memory) 122—and related actuators such as motors tomove the robotic arm 102. The electronics 114 in the base 101 may alsoprocess and transmit control signals communicated from the commandconsole.

In some embodiments, the base 101 includes wheels 115 to transport thesurgical robotic system 100. Mobility of the surgical robotic system 100helps accommodate space constraints in a surgical operating room as wellas facilitate appropriate positioning and movement of surgicalequipment. Further, the mobility allows the robotic arms 102 to beconfigured such that the robotic arms 102 do not interfere with thepatient, physician, anesthesiologist, or any other equipment. Duringprocedures, a user may control the robotic arms 102 using controldevices such as the command console.

In some embodiments, the robotic arm 102 includes set up joints that usea combination of brakes and counter-balances to maintain a position ofthe robotic arm 102. The counter-balances may include gas springs orcoil springs. The brakes, e.g., fail safe brakes, may be includemechanical and/or electrical components. Further, the robotic arms 102may be gravity-assisted passive support type robotic arms.

Each robotic arm 102 may be coupled to an instrument device manipulator(IDM) 117 using a mechanism changer interface (MCI) 116. The IDM 117 canbe removed and replaced with a different type of IDM, for example, afirst type of IDM manipulates an endoscope, while a second type of IDMmanipulates a laparoscope. The MCI 116 includes connectors to transferpneumatic pressure, electrical power, electrical signals, and opticalsignals from the robotic arm 102 to the IDM 117. The MCI 116 can be aset screw or base plate connector. The IDM 117 manipulates surgicalinstruments such as the endoscope 118 using techniques including directdrive, harmonic drive, geared drives, belts and pulleys, magneticdrives, and the like. The MCI 116 is interchangeable based on the typeof IDM 117 and can be customized for a certain type of surgicalprocedure. The robotic 102 arm can include a joint level torque sensingand a wrist at a distal end, such as the KUKA AG® LBR5 robotic arm.

The endoscope 118 is a tubular and flexible surgical instrument that isinserted into the anatomy of a patient to capture images of the anatomy(e.g., body tissue). In particular, the endoscope 118 includes one ormore imaging devices (e.g., cameras or other types of optical sensors)that capture the images. The imaging devices may include one or moreoptical components such as an optical fiber, fiber array, or lens. Theoptical components move along with the tip of the endoscope 118 suchthat movement of the tip of the endoscope 118 results in changes to theimages captured by the imaging devices. The endoscope 118 is furtherdescribed with reference to FIGS. 3A-4B in Section IV. Endoscope.

Robotic arms 102 of the surgical robotic system 100 manipulate theendoscope 118 using elongate movement members. The elongate movementmembers may include pull wires, also referred to as pull or push wires,cables, fibers, or flexible shafts. For example, the robotic arms 102actuate multiple pull wires coupled to the endoscope 118 to deflect thetip of the endoscope 118. The pull wires may include both metallic andnon-metallic materials such as stainless steel, Kevlar, tungsten, carbonfiber, and the like. The endoscope 118 may exhibit nonlinear behavior inresponse to forces applied by the elongate movement members. Thenonlinear behavior may be based on stiffness and compressibility of theendoscope 118, as well as variability in slack or stiffness betweendifferent elongate movement members.

FIGS. 1B-1F show various perspective views of the surgical roboticsystem 100 coupled to a robotic platform 150 (or surgical bed),according to various embodiments. Specifically, FIG. 1B shows a sideview of the surgical robotic system 100 with the robotic arms 102manipulating the endoscopic 118 to insert the endoscopic inside apatient's body, and the patient is lying on the robotic platform 150.FIG. 1C shows a top view of the surgical robotic system 100 and therobotic platform 150, and the endoscopic 118 manipulated by the roboticarms is inserted inside the patient's body. FIG. 1D shows a perspectiveview of the surgical robotic system 100 and the robotic platform 150,and the endoscopic 118 is controlled to be positioned horizontallyparallel with the robotic platform. FIG. 1E shows another perspectiveview of the surgical robotic system 100 and the robotic platform 150,and the endoscopic 118 is controlled to be positioned relativelyperpendicular to the robotic platform. In more detail, in FIG. 1E, theangle between the horizontal surface of the robotic platform 150 and theendoscopic 118 is 75 degree. FIG. 1F shows the perspective view of thesurgical robotic system 100 and the robotic platform 150 shown in FIG.1E, and in more detail, the angle between the endoscopic 118 and thevirtual line 160 connecting one end 180 of the endoscopic and therobotic arm 102 that is positioned relatively farther away from therobotic platform is 90 degree.

II. Command Console

FIG. 2 shows an example command console 200 for the example surgicalrobotic system 100, according to one embodiment. The command console 200includes a console base 201, display modules 202, e.g., monitors, andcontrol modules, e.g., a keyboard 203 and joystick 204. In someembodiments, one or more of the command console 200 functionality may beintegrated into a base 101 of the surgical robotic system 100 or anothersystem communicatively coupled to the surgical robotic system 100. Auser 205, e.g., a physician, remotely controls the surgical roboticsystem 100 from an ergonomic position using the command console 200.

The console base 201 may include a central processing unit, a memoryunit, a data bus, and associated data communication ports that areresponsible for interpreting and processing signals such as cameraimagery and tracking sensor data, e.g., from the endoscope 118 shown inFIG. 1. In some embodiments, both the console base 201 and the base 101perform signal processing for load-balancing. The console base 201 mayalso process commands and instructions provided by the user 205 throughthe control modules 203 and 204. In addition to the keyboard 203 andjoystick 204 shown in FIG. 2, the control modules may include otherdevices, for example, computer mice, trackpads, trackballs, controlpads, video game controllers, and sensors (e.g., motion sensors orcameras) that capture hand gestures and finger gestures.

The user 205 can control a surgical instrument such as the endoscope 118using the command console 200 in a velocity mode or position controlmode. In velocity mode, the user 205 directly controls pitch and yawmotion of a distal end of the endoscope 118 based on direct manualcontrol using the control modules. For example, movement on the joystick204 may be mapped to yaw and pitch movement in the distal end of theendoscope 118. The joystick 204 can provide haptic feedback to the user205. For example, the joystick 204 vibrates to indicate that theendoscope 118 cannot further translate or rotate in a certain direction.The command console 200 can also provide visual feedback (e.g., pop-upmessages) and/or audio feedback (e.g., beeping) to indicate that theendoscope 118 has reached maximum translation or rotation.

In position control mode, the command console 200 uses athree-dimensional (3D) map of a patient and pre-determined computermodels of the patient to control a surgical instrument, e.g., theendoscope 118. The command console 200 provides control signals torobotic arms 102 of the surgical robotic system 100 to manipulate theendoscope 118 to a target location. Due to the reliance on the 3D map,position control mode requires accurate mapping of the anatomy of thepatient.

In some embodiments, users 205 can manually manipulate robotic arms 102of the surgical robotic system 100 without using the command console200. During setup in a surgical operating room, the users 205 may movethe robotic arms 102, endoscopes 118, and other surgical equipment toaccess a patient. The surgical robotic system 100 may rely on forcefeedback and inertia control from the users 205 to determine appropriateconfiguration of the robotic arms 102 and equipment.

The display modules 202 may include electronic monitors, virtual realityviewing devices, e.g., goggles or glasses, and/or other means of displaydevices. In some embodiments, the display modules 202 are integratedwith the control modules, for example, as a tablet device with atouchscreen. Further, the user 205 can both view data and input commandsto the surgical robotic system 100 using the integrated display modules202 and control modules.

The display modules 202 can display 3D images using a stereoscopicdevice, e.g., a visor or goggle. The 3D images provide an “endo view”(i.e., endoscopic view), which is a computer 3D model illustrating theanatomy of a patient. The “endo view” provides a virtual environment ofthe patient's interior and an expected location of an endoscope 118inside the patient. A user 205 compares the “endo view” model to actualimages captured by a camera to help mentally orient and confirm that theendoscope 118 is in the correct—or approximately correct—location withinthe patient. The “endo view” provides information about anatomicalstructures, e.g., the shape of an intestine or colon of the patient,around the distal end of the endoscope 118. The display modules 202 cansimultaneously display the 3D model and computerized tomography (CT)scans of the anatomy the around distal end of the endoscope 118.Further, the display modules 202 may overlay the already determinednavigation paths of the endoscope 118 on the 3D model and scans/imagesgenerated based on preoperative model data (e.g., CT scans).

In some embodiments, a model of the endoscope 118 is displayed with the3D models to help indicate a status of a surgical procedure. Forexample, the CT scans identify a lesion in the anatomy where a biopsymay be necessary. During operation, the display modules 202 may show areference image captured by the endoscope 118 corresponding to thecurrent location of the endoscope 118. The display modules 202 mayautomatically display different views of the model of the endoscope 118depending on user settings and a particular surgical procedure. Forexample, the display modules 202 show an overhead fluoroscopic view ofthe endoscope 118 during a navigation step as the endoscope 118approaches an operative region of a patient.

III. Instrument Device Manipulator

FIG. 3A shows an isometric view of an example independent drivemechanism of the IDM 117 shown in FIG. 1, according to one embodiment.The independent drive mechanism can tighten or loosen the pull wires321, 322, 323, and 324 (e.g., independently from each other) of anendoscope by rotating the output shafts 305, 306, 307, and 308 of theIDM 117, respectively. Just as the output shafts 305, 306, 307, and 308transfer force down pull wires 321, 322, 323, and 324, respectively,through angular motion, the pull wires 321, 322, 323, and 324 transferforce back to the output shafts. The IDM 117 and/or the surgical roboticsystem 100 can measure the transferred force using a sensor, e.g., astrain gauge further described below.

FIG. 3B shows a conceptual diagram that shows how forces may be measuredby a strain gauge 334 of the independent drive mechanism shown in FIG.3A, according to one embodiment. A force 331 may direct away from theoutput shaft 305 coupled to the motor mount 333 of the motor 337.Accordingly, the force 331 results in horizontal displacement of themotor mount 333. Further, the strain gauge 334 horizontally coupled tothe motor mount 333 experiences strain in the direction of the force331. The strain may be measured as a ratio of the horizontaldisplacement of the tip 335 of strain gauge 334 to the overallhorizontal width 336 of the strain gauge 334.

In some embodiments, the IDM 117 includes additional sensors, e.g.,inclinometers or accelerometers, to determine an orientation of the IDM117. Based on measurements from the additional sensors and/or the straingauge 334, the surgical robotic system 100 can calibrate readings fromthe strain gauge 334 to account for gravitational load effects. Forexample, if the IDM 117 is oriented on a horizontal side of the IDM 117,the weight of certain components of the IDM 117 may cause a strain onthe motor mount 333. Accordingly, without accounting for gravitationalload effects, the strain gauge 334 may measure strain that did notresult from strain on the output shafts.

IV. Endoscope

FIG. 4A shows a top view of an example endoscope 118, according to oneembodiment. The endoscope 118 includes a leader 415 tubular componentnested or partially nested inside and longitudinally-aligned with asheath 411 tubular component. The sheath 411 includes a proximal sheathsection 412 and distal sheath section 413. The leader 415 has a smallerouter diameter than the sheath 411 and includes a proximal leadersection 416 and distal leader section 417. The sheath base 414 and theleader base 418 actuate the distal sheath section 413 and the distalleader section 417, respectively, for example, based on control signalsfrom a user of a surgical robotic system 100. The sheath base 414 andthe leader base 418 are, e.g., part of the IDM 117 shown in FIG. 1.

Both the sheath base 414 and the leader base 418 include drivemechanisms (e.g., the independent drive mechanism further described withreference to FIG. 3A-B in Section III. Instrument Device Manipulator) tocontrol pull wires coupled to the sheath 411 and leader 415. Forexample, the sheath base 414 generates tensile loads on pull wirescoupled to the sheath 411 to deflect the distal sheath section 413.Similarly, the leader base 418 generates tensile loads on pull wirescoupled to the leader 415 to deflect the distal leader section 417. Boththe sheath base 414 and leader base 418 may also include couplings forthe routing of pneumatic pressure, electrical power, electrical signals,or optical signals from IDMs to the sheath 411 and leader 415,respectively. A pull wire may include a steel coil pipe along the lengthof the pull wire within the sheath 411 or the leader 415, whichtransfers axial compression back to the origin of the load, e.g., thesheath base 414 or the leader base 418, respectively.

The endoscope 118 can navigate the anatomy of a patient with ease due tothe multiple degrees of freedom provided by pull wires coupled to thesheath 411 and the leader 415. For example, four or more pull wires maybe used in either the sheath 411 and/or the leader 415, providing eightor more degrees of freedom. In other embodiments, up to three pull wiresmay be used, providing up to six degrees of freedom. The sheath 411 andleader 415 may be rotated up to 360 degrees along a longitudinal axis406, providing more degrees of motion. The combination of rotationalangles and multiple degrees of freedom provides a user of the surgicalrobotic system 100 with a user friendly and instinctive control of theendoscope 118.

FIG. 4B illustrates an example endoscope tip 430 of the endoscope 118shown in FIG. 4A, according to one embodiment. In FIG. 4B, the endoscopetip 430 includes an imaging device 431 (e.g., a camera), illuminationsources 432, and ends of EM coils 434. The illumination sources 432provide light to illuminate an interior portion of an anatomical space.The provided light allows the imaging device 431 to record images ofthat space, which can then be transmitted to a computer system such ascommand console 200 for processing as described herein. Electromagnetic(EM) coils 434 located on the tip 430 may be used with an EM trackingsystem to detect the position and orientation of the endoscope tip 430while it is disposed within an anatomical system. In some embodiments,the coils may be angled to provide sensitivity to EM fields alongdifferent axes, giving the ability to measure a full 6 degrees offreedom: three positional and three angular. In other embodiments, onlya single coil may be disposed within the endoscope tip 430, with itsaxis oriented along the endoscope shaft of the endoscope 118; due to therotational symmetry of such a system, it is insensitive to roll aboutits axis, so only 5 degrees of freedom may be detected in such a case.The endoscope tip 430 further comprises a working channel 436 throughwhich surgical instruments, such as biopsy needles, may be insertedalong the endoscope shaft, allowing access to the area near theendoscope tip.

V. Registration Transform of EM System to 3D Model

V. A. Schematic Setup of an EM Tracking System

FIG. 5 shows an example schematic setup of an EM tracking system 505included in a surgical robotic system 500, according to one embodiment.In FIG. 5, multiple robot components (e.g., window field generator,reference sensors as described below) are included in the EM trackingsystem 505. The robotic surgical system 500 includes a surgical bed 511to hold a patient's body. Beneath the bed 511 is the window fieldgenerator (WFG) 512 configured to sequentially activate a set of EMcoils (e.g., the EM coils 434 shown in FIG. 4B). The WFG 512 generatesan alternating current (AC) magnetic field over a wide volume; forexample, in some cases it may create an AC field in a volume of about0.5×0.5×0.5 m.

Additional fields may be applied by further field generators to aid intracking instruments within the body. For example, a planar fieldgenerator (PFG) may be attached to a system arm adjacent to the patientand oriented to provide an EM field at an angle. Reference sensors 513may be placed on the patient's body to provide local EM fields tofurther increase tracking accuracy. Each of the reference sensors 513may be attached by cables 514 to a command module 515. The cables 514are connected to the command module 515 through interface units 516which handle communications with their respective devices as well asproviding power. The interface unit 516 is coupled to a system controlunit (SCU) 517 which acts as an overall interface controller for thevarious entities mentioned above. The SCU 517 also drives the fieldgenerators (e.g., WFG 512), as well as collecting sensor data from theinterface units 516, from which it calculates the position andorientation of sensors within the body. The SCU 517 may be coupled to apersonal computer (PC) 518 to allow user access and control.

The command module 515 is also connected to the various IDMs 519 coupledto the surgical robotic system 500 as described herein. The IDMs 519 aretypically coupled to a single surgical robotic system (e.g., thesurgical robotic system 500) and are used to control and receive datafrom their respective connected robotic components; for example, roboticendoscope tools or robotic arms. As described above, as an example, theIDMs 519 are coupled to an endoscopic tool (not shown here) of thesurgical robotic system 500.

The command module 515 receives data passed from the endoscopic tool.The type of received data depends on the corresponding type ofinstrument attached. For example, example received data includes sensordata (e.g., image data, EM data), robot data (e.g., endoscopic and IDMphysical motion data), control data, and/or video data. To better handlevideo data, a field-programmable gate array (FPGA) 520 may be configuredto handle image processing. Comparing data obtained from the varioussensors, devices, and field generators allows the SCU 517 to preciselytrack the movements of different components of the surgical roboticsystem 500, and for example, positions and orientations of thesecomponents.

In order to track a sensor through the patient's anatomy, the EMtracking system 505 may require a process known as “registration,” wherethe system finds the geometric transformation that aligns a singleobject between different coordinate systems. For instance, a specificanatomical site on a patient has two different representations in the 3Dmodel coordinates and in the EM sensor coordinates. To be able toestablish consistency and common language between these two differentcoordinate systems, the EM tracking system 505 needs to find thetransformation that links these two representations, i.e., registration.For example, the position of the EM tracker relative to the position ofthe EM field generator may be mapped to a 3D coordinate system toisolate a location in a corresponding 3D model.

V. B. 3D Model Representation

FIGS. 6A-6B show an example anatomical lumen 600 and an example 3D model620 of the anatomical lumen, according to one embodiment. Morespecifically, FIGS. 6A-6B illustrate the relationships of centerlinecoordinates, diameter measurements and anatomical spaces between theactual anatomical lumen 600 and its 3D model 620. In FIG. 6A, theanatomical lumen 600 is roughly tracked longitudinally by centerlinecoordinates 601, 602, 603, 604, 605, and 606 where each centerlinecoordinate roughly approximates the center of the tomographic slice ofthe lumen. The centerline coordinates are connected and visualized by acenterline 607. The volume of the lumen can be further visualized bymeasuring the diameter of the lumen at each centerline coordinate, e.g.,coordinates 608, 609, 610, 611, 612, and 613 represent the measurementsof the lumen 600 corresponding to coordinates 601, 602, 603, 604, 605,and 606.

FIG. 6B shows the example 3D model 620 of the anatomical lumen 600 shownin FIG. 6A, according to one embodiment. In FIG. 6B, the anatomicallumen 600 is visualized in 3D space by first locating the centerlinecoordinates 601, 602, 603, 604, 605, and 606 in 3D space based on thecenterline 607. As one example, at each centerline coordinate, the lumendiameter is visualized as a 2D circular space (e.g., the 2D circularspace 630) with diameters 608, 609, 610, 611, 612, and 613. Byconnecting those 2D circular spaces to form a 3D space, the anatomicallumen 600 is approximated and visualized as the 3D model 620. Moreaccurate approximations may be determined by increasing the resolutionof the centerline coordinates and measurements, i.e., increasing thedensity of centerline coordinates and measurements for a given lumen orsubsection. Centerline coordinates may also include markers to indicatepoint of interest for the physician, including lesions.

In some embodiments, a pre-operative software package is also used toanalyze and derive a navigation path based on the generated 3D model ofthe anatomical space. For example, the software package may derive ashortest navigation path to a single lesion (marked by a centerlinecoordinate) or to several lesions. This navigation path may be presentedto the operator intra-operatively either in two-dimensions orthree-dimensions depending on the operator's preference. In certainimplementations, as discussed below, the navigation path (or at aportion thereof) may be pre-operatively selected by the operator. Thepath selection may include identification of one or more targetlocations (also simply referred to as a “target”) within the patient'sanatomy.

FIG. 7 shows a computer-generated 3D model 700 representing ananatomical space, according to one embodiment. As discussed above inFIGS. 6A-6B, the 3D model 700 may be generated using a centerline 701that was obtained by reviewing CT scans that were generatedpreoperatively. In some embodiments, computer software may be able tomap a navigation path 702 within the tubular network to access anoperative site 703 (or other target) within the 3D model 700. In someembodiments, the operative site 703 may be linked to an individualcenterline coordinate 704, which allows a computer algorithm totopologically search the centerline coordinates of the 3D model 700 forthe optimum path 702 within the tubular network. In certain embodiments,the topological search for the path 702 may be constrained by certainoperator selected parameters, such as the location of one or moretargets, one or more waypoints, etc.

In some embodiments, the distal end of the endoscopic tool within thepatient's anatomy is tracked, and the tracked location of the endoscopictool within the patient's anatomy is mapped and placed within a computermodel, which enhances the navigational capabilities of the tubularnetwork. In order to track the distal working end of the endoscopictool, i.e., location and orientation of the working end, a number ofapproaches may be employed, either individually or in combination.

In a sensor-based approach to localization, a sensor, such as anelectromagnetic (EM) tracker, may be coupled to the distal working endof the endoscopic tool to provide a real-time indication of theprogression of the endoscopic tool. In EM-based tracking, an EM tracker,embedded in the endoscopic tool, measures the variation in theelectromagnetic field created by one or more EM transmitters. Thetransmitters (or field generators), may be placed close to the patient(e.g., as part of the surgical bed) to create a low intensity magneticfield. This induces small-currents in sensor coils in the EM tracker,which are correlated to the distance and angle between the sensor andthe generator. The electrical signal may then be digitized by aninterface unit (on-chip or PCB) and sent via cables/wiring back to thesystem cart and then to the command module. The data may then beprocessed to interpret the current data and calculate the preciselocation and orientation of the sensor relative to the transmitters.Multiple sensors may be used at different locations in the endoscopictool, for instance in leader and sheath in order to calculate theindividual positions of those components. Accordingly, based on readingsfrom an artificially-generated EM field, the EM tracker may detectchanges in field strength as it moves through the patient's anatomy.

V. C. On-the-Fly EM Registration

FIGS. 8A-8D show example graphs 810-840 illustrating on-the-flyregistration of an EM system to a 3D model of a path through a tubularnetwork, according to one embodiment. The navigation configurationsystem described herein allows for on-the-fly registration of the EMcoordinates to the 3D model coordinates without the need for independentregistration prior to an endoscopic procedure. In more detail, FIG. 8Ashows that the coordinate systems of the EM tracking system and the 3Dmodel are initially not registered to each other, and the graph 810 inFIG. 8A shows the registered (or expected) location of an endoscope tip801 moving along a planned navigation path 802 through a branchedtubular network (not shown here), and the registered location of theinstrument tip 801 as well as the planned path 802 are derived from the3D model. The actual position of the tip is repeatedly measured by theEM tracking system 505, resulting in multiple measured location datapoints 803 based on EM data. As shown in FIG. 8A, the data points 803derived from EM tracking are initially located far from the registeredlocation of the endoscope tip 801 expected from the 3D model, reflectingthe lack of registration between the EM coordinates and the 3D modelcoordinates. There may be several reasons for this, for example, even ifthe endoscope tip is being moved relatively smoothly through the tubularnetwork, there may still be some visible scatter in the EM measurement,due to breathing movement of the lungs of the patient.

The points on the 3D model may also be determined and adjusted based oncorrelation between the 3D model itself, image data received fromoptical sensors (e.g., cameras) and robot data from robot commands. The3D transformation between these points and collected EM data points willdetermine the initial registration of the EM coordinate system to the 3Dmodel coordinate system.

FIG. 8B shows a graph 820 at a later temporal stage compared with thegraph 810, according to one embodiment. More specifically, the graph 820shows the expected location of the endoscope tip 801 expected from the3D model has been moved farther along the preplanned navigation path802, as illustrated by the shift from the original expected position ofthe instrument tip 801 shown in FIG. 8A along the path to the positionshown in FIG. 8B. During the EM tracking between generation of the graph810 and generation of graph 820, additional data points 803 have beenrecorded by the EM tracking system but the registration has not yet beenupdated based on the newly collected EM data. As a result, the datapoints 803 in FIG. 8B are clustered along a visible path 814, but thatpath differs in location and orientation from the planned navigationpath 802 the endoscope tip is being directed by the operator to travelalong. Eventually, once sufficient data (e.g., EM data) is accumulated,compared with using only the 3D model or only the EM data, a relativelymore accurate estimate can be derived from the transform needed toregister the EM coordinates to those of the 3D model. The determinationof sufficient data may be made by threshold criteria such as total dataaccumulated or number of changes of direction. For example, in abranched tubular network such as a bronchial tube network, it may bejudged that sufficient data have been accumulated after arriving at twobranch points.

FIG. 8C shows a graph 830 shortly after the navigation configurationsystem has accumulated a sufficient amount of data to estimate theregistration transform from EM to 3D model coordinates, according to oneembodiment. The data points 803 in FIG. 8C have now shifted from theirprevious position as shown in FIG. 8B as a result of the registrationtransform. As shown in FIG. 8C, the data points 803 derived from EM datais now falling along the planned navigation path 802 derived from the 3Dmodel, and each data point among the data points 803 is now reflecting ameasurement of the expected position of endoscope tip 801 in thecoordinate system of the 3D model. In some embodiments, as further dataare collected, the registration transform may be updated to increaseaccuracy. In some cases, the data used to determine the registrationtransformation may be a subset of data chosen by a moving window, sothat the registration may change over time, which gives the ability toaccount for changes in the relative coordinates of the EM and 3Dmodels—for example, due to movement of the patient.

FIG. 8D shows an example graph 840 in which the expected location of theendoscope tip 801 has reached the end of the planned navigation path802, arriving at the target location in the tubular network, accordingto one embodiment. As shown in FIG. 8D, the recorded EM data points 803is now generally tracks along the planned navigation path 802, whichrepresents the tracking of the endoscope tip throughout the procedure.Each data point reflects a transformed location due to the updatedregistration of the EM tracking system to the 3D model.

In some embodiments, each of the graphs shown in FIGS. 8A-8D can beshown sequentially on a display visible to a user as the endoscope tipis advanced in the tubular network. In some embodiments, the processorcan be configured with instructions from the navigation configurationsystem such that the model shown on the display remains substantiallyfixed when the measured data points are registered to the display byshifting of the measured path shown on the display in order to allow theuser to maintain a fixed frame of reference and to remain visuallyoriented on the model and on the planned path shown on the display.

FIGS. 8E-8F show the effect of an example registration of the EM systemto a 3D model of a branched tubular network, according to oneembodiment. In FIGS. 8E-8F, 3D graphs showing electromagnetic trackingdata 852 and a model of a patient's bronchial system 854 are illustratedwithout (shown in FIG. 8E) and with (shown in FIG. 8F) a registrationtransform. In FIG. 8E, without registration, tracking data 860 have ashape that corresponds to a path through the bronchial system 854, butthat shape is subjected to an arbitrary offset and rotation. In FIG. 8F,by applying the registration, the tracking data 852 are shifted androtated, so that they correspond to a path through the bronchial system854.

VI. Navigation Configuration System

VI. A. High-Level Overview of Navigation Configuration System

FIGS. 9A-9B show example block diagrams of a navigation configurationsystem 900, according to one embodiment. More specifically, FIG. 9Ashows a high-level overview of an example block diagram of thenavigation configuration system 900, according to one embodiment. InFIG. 9A, the navigation configuration system 900 includes multiple inputdata stores, a navigation module 905 that receives various types ofinput data from the multiple input data stores, an outside segmentationnavigation module 905 that receives various types of input data from themultiple input data stores, and an output navigation data store 990 thatreceives output navigation data from the navigation module. The blockdiagram of the navigation configuration system 900 shown in FIG. 9A ismerely one example, and in alternative embodiments not shown, thenavigation configuration system 900 can include different and/oraddition entities. Likewise, functions performed by various entities ofthe system 900 may differ according to different embodiments. Thenavigation configuration system 900 may be similar to the navigationalsystem described in U.S. Patent Publication No. 2017/0084027, publishedon Mar. 23, 2017, the entirety of which is incorporated herein byreference.

The input data, as used herein, refers to raw data gathered from and/orprocessed by input devices (e.g., command module, optical sensor, EMsensor, IDM) for generating estimated state information for theendoscope as well as output navigation data. The multiple input datastores 910-945 include an image data store 910, an EM data store 920, arobot data store 930, a 3D model data store 940, and a path data store945. Each type of the input data stores 910-945 stores thename-indicated type of data for access and use by a navigation module905. Image data may include one or more image frames captured by theimaging device at the instrument tip, as well as information such asframe rates or timestamps that allow a determination of the time elapsedbetween pairs of frames. Robot data may include data related to physicalmovement of the medical instrument or part of the medical instrument(e.g., the instrument tip or sheath) within the tubular network. Examplerobot data includes command data instructing the instrument tip to reacha specific anatomical site and/or change its orientation (e.g., with aspecific pitch, roll, yaw, insertion, and retraction for one or both ofa leader and a sheath) within the tubular network, insertion datarepresenting insertion movement of the part of the medical instrument(e.g., the instrument tip or sheath), IDM data, and mechanical datarepresenting mechanical movement of an elongate member of the medicalinstrument, for example motion of one or more pull wires, tendons orshafts of the endoscope that drive the actual movement of the medialinstrument within the tubular network. EM data may be collected by EMsensors and/or the EM tracking system as described above. 3D model datamay be derived from 2D CT scans as described above. Path data includesthe planned navigation path (e.g., the navigation path 702) which may begenerated by a topological search of the tubular network to one or moretargets. The multiple input data stores may also include other types ofdata stores such as an optional position sensor data store 947. Incertain implementations, the position sensor data store 947 may storeshape sensor data received from a shape sensing fiber positioned withinthe instrument. The navigation module 905 and/or the outsidesegmentation navigation module 907 may be configured to receive theposition sensor data from the position sensor data store 947 dependingon the embodiment.

The output navigation data store 990 receives and stores outputnavigation data provided by the navigation module 905 and/or the outsidesegmentation navigation module 907. As described in greater detailbelow, the system 900 may adjust the weights given to the outputnavigation data generated by the navigation module 905 and the outsidesegmentation navigation module 907 based on the position of theinstrument with respect to the mapped portion of the luminal network.Output navigation data indicates information to assist in directing themedical instrument through the tubular network to arrive at a particulardestination within the tubular network, and is based on estimated stateinformation for the medical instrument at each instant time, theestimated state information including the location and orientation ofthe medical instrument within the tubular network. In one embodiment, asthe medical instrument moves inside the tubular network, the outputnavigation data indicating updates of movement and location/orientationinformation of the medical instrument is provided in real time, whichbetter assists its navigation through the tubular network.

To determine the output navigation data, the navigation module 905and/or the outside segmentation navigation module 907 locates (ordetermines) the estimated state of the medical instrument within atubular network. As shown in FIG. 9A, the navigation module 905 furtherincludes various algorithm modules, such as an EM-based algorithm module950, an image-based algorithm module 960, a robot-based algorithm module970, and a path-based algorithm module 975, that each may consume mainlycertain types of input data and contribute a different type of data to astate estimator 980. As illustrated in FIG. 9A, the different kinds ofdata output by these modules, labeled EM-based data, the image-baseddata, the robot-based data, and the path-based data, may be generallyreferred to as “intermediate data” for sake of explanation. The detailedcomposition of each algorithm module and of the state estimator 980 ismore fully described below.

VI. B. Navigation Module

With reference to the navigation module 905 shown in FIG. 9A, thenavigation module 905 includes a state estimator 980 as well as multiplealgorithm modules that employ different algorithms for navigatingthrough a tubular network. For clarity of description, the stateestimator 980 is described first, followed by the description of thevarious modules that exchange data with the state estimator 980.

VI. B. 1 State Estimator

The state estimator 980 included in the navigation module 905 receivesvarious intermediate data and provides the estimated state of theinstrument tip as a function of time, where the estimated stateindicates the estimated location and orientation information of theinstrument tip within the tubular network. The estimated state data arestored in the estimated data store 985 that is included in the stateestimator 980.

FIG. 9B shows an example block diagram of the estimated state data store985 included in the state estimator 980, according to one embodiment.The estimated state data store 985 may include a bifurcation data store1086, a position data store 1087, a depth data store 1088, and anorientation data store 1089, however this particular breakdown of datastorage is merely one example, and in alternative embodiments not shown,different and/or additional data stores can be included in the estimatedstate data store 985.

The various stores introduced above represent estimated state data in avariety of ways. Specifically, bifurcation data refers to the locationof the medical instrument with respect to the set of branches (e.g.,bifurcation, trifurcation or a division into more than three branches)within the tubular network. For example, the bifurcation data can be setof branch choices elected by the instrument as it traverses through thetubular network, based on a larger set of available branches asprovided, for example, by the 3D model which maps the entirety of thetubular network. The bifurcation data can further include information infront of the location of the instrument tip, such as branches(bifurcations) that the instrument tip is near but has not yet traversedthrough, but which may have been detected, for example, based on thetip's current position information relative to the 3D model, or based onimages captured of the upcoming bifurcations.

Position data indicates three-dimensional position of some part of themedical instrument within the tubular network or some part of thetubular network itself. Position data can be in the form of absolutelocations or relative locations relative to, for example, the 3D modelof the tubular network. As one example, position data can include anindication of the position of the location of the instrument beingwithin a specific branch. The identification of the specific branch mayalso be stored as a segment identification (ID) which uniquelyidentifies the specific segment of the model in which the instrument tipis located.

Depth data indicates depth information of the instrument tip within thetubular network. Example depth data includes the total insertion(absolute) depth of the medical instrument into the patient as well asthe (relative) depth within an identified branch (e.g., the segmentidentified by the position data store 1087). Depth data may bedetermined based on position data regarding both the tubular network andmedical instrument.

Orientation data indicates orientation information of the instrumenttip, and may include overall roll, pitch, and yaw in relation to the 3Dmodel as well as pitch, roll, raw within an identified branch.

Turning back to FIG. 9A, the state estimator 980 provides the estimatedstate data back to the algorithm modules for generating more accurateintermediate data, which the state estimator uses to generate improvedand/or updated estimated states, and so on forming a feedback loop. Forexample, as shown in FIG. 9A, the EM-based algorithm module 950 receivesprior EM-based estimated state data, also referred to as data associatedwith timestamp “t−1.” The state estimator 980 uses this data to generate“estimated state data (prior)” that is associated with timestamp “t−1.”The state estimator 980 then provides the data back to the EM-basedalgorithm module. The “estimated state data (prior)” may be based on acombination of different types of intermediate data (e.g., robotic data,image data) that is associated with timestamp “t−1” as generated andreceived from different algorithm modules. Next, the EM-based algorithmmodule 950 runs its algorithms using the estimated state data (prior) tooutput to the state estimator 980 improved and updated EM-basedestimated state data, which is represented by “EM-based estimated statedata (current)” here and associated with timestamp t. This processcontinues to repeat for future timestamps as well.

As the state estimator 980 may use several different kinds ofintermediate data to arrive at its estimates of the state of the medicalinstrument within the tubular network, the state estimator 980 isconfigured to account for the various different kinds of errors anduncertainty in both measurement and analysis that each type ofunderlying data (robotic, EM, image, path) and each type of algorithmmodule might create or carry through into the intermediate data used forconsideration in determining the estimated state. To address these, twoconcepts are discussed, that of a probability distribution and that ofconfidence value.

The “probability” of the “probability distribution”, as used herein,refers to a likelihood of an estimation of a possible location and/ororientation of the medical instrument being correct. For example,different probabilities may be calculated by one of the algorithmmodules indicating the relative likelihood that the medical instrumentis in one of several different possible branches within the tubularnetwork. In one embodiment, the type of probability distribution (e.g.,discrete distribution or continuous distribution) is chosen to matchfeatures of an estimated state (e.g., type of the estimated state, forexample continuous position information vs. discrete branch choice). Asone example, estimated states for identifying which segment the medicalinstrument is in for a trifurcation may be represented by a discreteprobability distribution, and may include three discrete values of 20%,30% and 50% representing chance as being in the location inside each ofthe three branches as determined by one of the algorithm modules. Asanother example, the estimated state may include a roll angle of themedical instrument of 40±5 degrees and a segment depth of the instrumenttip within a branch may be is 4±1 mm, each represented by a Gaussiandistribution which is a type of continuous probability distribution.Different methods or modalities can be used to generate theprobabilities, which will vary by algorithm module as more fullydescribed below with reference to later figures.

In contrast, the “confidence value,” as used herein, reflects a measureof confidence in the estimation of the state provided by one of thealgorithms based one or more factors. For the EM-based algorithms,factors such as distortion to EM Field, inaccuracy in EM registration,shift or movement of the patient, and respiration of the patient mayaffect the confidence in estimation of the state. Particularly, theconfidence value in estimation of the state provided by the EM-basedalgorithms may depend on the particular respiration cycle of thepatient, movement of the patient or the EM field generators, and thelocation within the anatomy where the instrument tip locates. For theimage-based algorithms, examples factors that may affect the confidencevalue in estimation of the state include illumination condition for thelocation within the anatomy where the images are captured, presence offluid, tissue, or other obstructions against or in front of the opticalsensor capturing the images, respiration of the patient, condition ofthe tubular network of the patient itself (e.g., lung) such as thegeneral fluid inside the tubular network and occlusion of the tubularnetwork, and specific operating techniques used in, e.g., navigating orimage capturing.

For example one factor may be that a particular algorithm has differinglevels of accuracy at different depths in a patient's lungs, such thatrelatively close to the airway opening, a particular algorithm may havea high confidence in its estimations of medical instrument location andorientation, but the further into the bottom of the lung the medicalinstrument travels that confidence value may drop. Generally, theconfidence value is based on one or more systemic factors relating tothe process by which a result is determined, whereas probability is arelative measure that arises when trying to determine the correct resultfrom multiple possibilities with a single algorithm based on underlyingdata.

As one example, a mathematical equation for calculating results of anestimated state represented by a discrete probability distribution(e.g., branch/segment identification for a trifurcation with threevalues of an estimated state involved) can be as follows:S ₁ =C _(EM) *P _(1,EM) =C _(Image) *P _(1,Image) +C _(Robot) *P_(1,Robot);S ₂ =C _(EM) *P _(2,EM) =C _(Image) *P _(2,Image) +C _(Robot) *P_(2,Robot);S ₃ =C _(EM) *P _(3,EM) =C _(Image) *P _(3,Image) +C _(Robot) *P_(3,Robot);

In the example mathematical equation above, S_(i)(i=1, 2, 3) representspossible example values of an estimated state in a case where 3 possiblesegments are identified or present in the 3D model, C_(EM), C_(Image),and C_(Robot) represents confidence value corresponding to EM-basedalgorithm, image-based algorithm, and robot-based algorithm andP_(i,EM), P_(i,Image), and P_(i,Robot) represent the probabilities forsegment i.

To better illustrate the concepts of probability distributions andconfidence value associated with estimate states, a detailed example isprovided here. In this example, a user is trying to identify segmentwhere an instrument tip is located in a certain trifurcation within acentral airway (the predicted region) of the tubular network, and threealgorithms modules are used including EM-based algorithm, image-basedalgorithm, and robot-based algorithm. In this example, a probabilitydistribution corresponding to the EM-based algorithm may be 20% in thefirst branch, 30% in the second branch, and 50% in the third (last)branch, and the confidence value applied to this EM-based algorithm andthe central airway is 80%. For the same example, a probabilitydistribution corresponding to the image-based algorithm may be 40%, 20%,40% for the first, second, and third branch, and the confidence valueapplied to this image-based algorithm is 30%; while a probabilitydistribution corresponding to the robot-based algorithm may be 10%, 60%,30% for the first, second, and third branch, and the confidence valueapplied to this image-based algorithm is 20%. The difference ofconfidence values applied to the EM-based algorithm and the image-basedalgorithm indicates that the EM-based algorithm may be a better choicefor segment identification in the central airway, compared with theimage-based algorithm. An example mathematical calculation of a finalestimated state can be:

for the first branch: 20%*80%+40%*30%+10%*20%=30%; for the secondbranch: 30%*80%+20%*30%+60%*20%=42%; and for the third branch:50%*80%+40%*30%+30%*20%=58%.

In this example, the output estimated state for the instrument tip canbe the result values (e.g., the resulting 30%, 42% and 58%), orderivative value from these result values such as the determination thatthe instrument tip is in the third branch. Although this exampledescribes the use of the algorithm modules include EM-based algorithm,image-based algorithm, and robot-based algorithm, the estimation of thestate for the instrument tip can also be provided based on differentcombinations of the various algorithms modules, including the path-basedalgorithm.

As above the estimated state may be represented in a number of differentways. For example, the estimated state may further include an absolutedepth from airway to location of the tip of the instrument, as well as aset of data representing the set of branches traversed by the instrumentwithin the tubular network, the set being a subset of the entire set ofbranches provided by the 3D model of the patient's lungs, for example.The application of probability distribution and confidence value onestimated states allows improved accuracy of estimation of locationand/or orientation of the instrument tip within the tubular network.

VI. B. 2 Overview of Path-Based Navigation

As shown in FIG. 9A, the algorithm modules include an EM-based algorithmmodule 950, an image-based algorithm module 960, a robot-based algorithmmodule 970, and a path-based algorithm module 975. The algorithm modulesshown in FIG. 9A is merely one example, and in alternative embodiments,different and/additional algorithm modules involving different and/oradditional navigation algorithms can also be included in the navigationmodule 905. Further details and example embodiments of the EM-basedalgorithm module 950, the image-based algorithm module 960, and therobot-based algorithm module 970 are described in U.S. PatentPublication No. 2017/0084027, referenced above.

FIG. 10 shows an example block diagram of the path-based algorithmmodule 975 in accordance with aspects of this disclosure. The path-basedalgorithm module 975 receives as input, estimated state data (prior)(e.g., position data and/or depth data) from the estimated state datastore 985, the 3D model data from the 3D model data store 940, and thepath data from the path data store 945. Based on the received data, thepath-based algorithm module 975 determines an estimate of the positionof the instrument tip relative to the 3D model of the tubular networkand provides path-based estimated state data (current) to the stateestimator 980, which can be stored in the estimated state data store985. As an example, the path-based estimated state data may berepresented as a probability distribution between a plurality ofidentified segments of the 3D model (e.g., a discrete distribution of30% and 70% for two segments joined at a bifurcation).

The navigation configuration system 900 may operate in one of aplurality of modalities depending on the current location of theinstrument tip, which may be defined based on the estimated state data(prior) received from the estimated state data store 985. Specifically,the navigation configuration system 900 may operate in one modality(e.g., using navigation module 905) when the current location of theinstrument tip is determined to be within a mapped portion of theluminal network, which may be defined by the 3D model data stored in the3D model data store 940. Further, in certain implementations, thepath-based algorithm module 975 may operate in another modality (e.g.,using outside segmentation navigation module 907) when the currentlocation of the instrument tip is determined to be outside of the mappedportion of the luminal network or within a threshold distance of theunmapped portion of the luminal network. As will be described in greaterdetail below, the navigation configuration system 900 may transitionbetween the first and second modalities based on the detection ofcertain threshold values, such as, the distance from the currentlocation of the instrument to the edge of the mapped portion of theluminal network.

VI. B. 2. I. Path-Based Navigation—Within Mapped Portion of LuminalNetwork

FIG. 11 is a flowchart illustrating an example method operable by arobotic system, or component(s) thereof, for path-based navigation oftubular networks in accordance with aspects of this disclosure. Forexample, the steps of method 1100 illustrated in FIG. 11 may beperformed by processor(s) and/or other component(s) of a medical roboticsystem (e.g., surgical robotic system 500) or associated system(s)(e.g., the path-based algorithm module 945 of the navigationconfiguration system 900). For convenience, the method 1100 is describedas performed by the navigation configuration system, also referred tosimply as the “system” in connection with the description of the method1100.

The method 1100 begins at block 1101. At block 1105, the system mayreceive location data from at least one of a set of location sensors anda set of robot command inputs. The location data may be indicative of alocation of an instrument configured to be driven through a luminalnetwork of a patient. As described above, the system may include atleast one computer-readable memory having stored thereon a model of theluminal network (e.g., the 3D model data stored in 3D model data store940), a position of a target with respect to the model, and a path alongat least a portion of the model from an access point to the target. Incertain embodiments, the path and the position of the target may bestored as path data in the path data store 945.

At block 1110, the system may determine a first estimate of the locationof the instrument at a first time based on the location data. The firstestimate of the location of the instrument may be based on, for example,data received from one or more of the image data store 910, the EM datastore 920, the robot data store 930, and/or the 3D model data store 940.

At block 1115, the system may determine a second estimate of thelocation of the instrument at the first time based on the path. Incertain implementations, the path-based estimated state data may includean indication of a segment along the path (e.g., path data received fromthe path data store 945) and a weight associated with the identifiedsegment. Thus, the system may determine a weight associated with thepath-based location estimate.

Depending on the embodiment, the system may select a segment of theluminal network along the path as the estimated location of theinstrument based on depth data received from the depth data store 1088of the estimated state data store 985. The system may, using the depthinformation, estimate the location of the instrument based on a distancealong the path determined from the depth data (e.g., a distance definedby the depth data starting from the access point of the luminalnetwork).

The system may employ one of a plurality of methods or modalities todetermine the weight associated with the path-based estimate. In certainembodiments, the system may determine the weight based on the locationof the instrument within the luminal network (e.g., based on theestimated state data (prior) received from the estimated state datastore 985). As described in detail below, the weight associated with thepath-based location estimate may be based on the probability that theoperator will deviate from the path while driving the instrument.Various factors may influence the probability that the operator willdrive the instrument down a segment of the luminal network that is notpart of the path. Examples of these factors include: difficulty invisually identifying the correct segment for advancing the instrument,complexity of the branching system of the luminal network, operatordetermination to explore portions of the luminal network outside of thepath, etc. Some or all of these factors may increase the probabilitythat the operator will deviate from the path according to the insertiondepth of the instrument into the luminal network. By proper selection ofthe weight, the system may increase the ability of the state estimator980 to reliably use the path-based location estimation as a source ofdata on which to base the estimated state of the instrument.

In related aspects, further details and an example model relating toblock 1115 are described below with reference to FIG. 12.

Continuing with FIG. 11, at block 1120, the system may determine thelocation of the instrument at the first time based on the first estimateand the second estimate. This determination may be performed, forexample, by the state estimator 980 determining the state of theinstrument based on estimated state data received from the path-basedalgorithm module 975 and at least one: of the EM-based algorithm module950, the image-based algorithm module 960, and the robot-based algorithmmodule 970. In embodiments where the system determines a weightassociated with the path-based location estimate, the system may furtheruse the weight in determining the location of the instrument at block1120. The method 1110 ends at block 1125.

FIG. 12 is a simplified example model of a portion of a luminal networkfor describing aspects of this disclosure related to path-based locationestimation. In particular, FIG. 12 depicts a model 1200 of a simplifiedluminal network including a skeleton 1205, which may be defined by acenterline of the luminal network, and a navigational path 1210 whichtraverses a portion of the model 1200. Although illustrated as offsetfrom the skeleton 1205, the navigational path 1210 may be defined alongthe skeleton 1205 in certain embodiments. The model 1200 furtherincludes a first-generation segment 1221, two second-generation segments1231 and 1235 which branch from the first-generation segment 1221, andfour third-generation segments 1241, 1243, 1245, and 1247. Two examplelocations 1251 and 1255 of a distal end of an instrument are alsoillustrated in FIG. 12.

In one implementation, the location of the instrument may be defined byidentifying the segment in which the distal end of the instrument iscurrently located. In this implementation, the model may include aplurality of segments (e.g., segments 1221-1247 as illustrated in FIG.12), where each segment is associated with a generation count orgeneration designation. The generation count of a given segment may bedetermined or defined based on the number of branches in the luminalnetwork located between the given segment and an access point of thepatient allowing the instrument access into the luminal network. In theFIG. 12 embodiment, an example assignment of generation counts to thesegments 1221-1247 may include: the first generation segment 1221 havinga generation count of one, the second generation segments 1231 and 1235having a generation count of two, and the third generation segments1241, 1243, 1245, and 1247 having a generation count of three. Thoseskilled in the art will recognize that other numbering schemes may beemployed to assign generation counts and/or generation designations tothe segments of a luminal network.

In certain implementations, the system may estimate a current segment inwhich the instrument is located using a path-based location estimate anddetermine the weight associated with the path-based location estimatebased on the generation count of the current segment. For example, whenthe instrument is positioned at the first location 1251, the segmentcount of the current segment 1231 may be two. The system may, in certainembodiments, decrease the weight for the path-based location estimate asthe generation count increases. In other words, the weight given to thepath-based estimate may be decreased (e.g., monotonically) as thegeneration count of the current segment increases. Referring to theexample of FIG. 12, in this implementation the weight assigned to thepath-based estimate at the second location 1255 may be less than theweight assigned to the path-based estimate at the first location 1251.The particular function used by the system to determine the weight isnot particularly limited. In one example implementation, the weightgiven to a particular segment may be inversely proportional to thesegment's generation count.

After the instrument has been advanced a sufficient distance into theluminal network, the system may reduce the weight assigned to thepath-based location estimate to zero or another minimal value. Incertain implementations, the system may determine whether to reduce theweight to zero or the minimal value based on the generation count of thecurrent segment in which the instrument is located. For example, thesystem may determine that the generation count of the current segment isgreater than a threshold generation count, and set the weight to zero,or the minimal value, in response to determining that the generationcount of the first segment is greater than the threshold generationcount.

Depending on the embodiment, the weight associated with the path-basedlocation estimate may correspond to a confidence value associated withthe estimate. As described above, the confidence value may reflect ameasure of confidence in the estimation of the state provided by thepath-based algorithm module 975. The system may determine the confidencevalue based on the likelihood that the operator of the robotic systemwill deviate from the navigation path. The likelihood that the operatorwill deviate from the navigation path may be determined empiricallybased on tracking the location of the instrument during actual medicalprocedures. In one example, the likelihood that the operator willdeviate from the navigation path near the start of the procedure may bepractically zero, such as when the operator is transitioning from thetrachea to one of the main bronchi during a bronchoscopic procedure.However, as the instrument is advanced further into the airway, it maybe more difficult for the operator to identify the correct segment ofthe network to drive the instrument into based on, for example, theimages received from the camera. Alternatively, the operator may decideto deviate from the path when as the instrument approaches the target inorder to investigate a portion of the luminal network or to perform acomplex navigational maneuver (e.g., articulating the instrument arounda tight curvature in the luminal network). Thus, it may be advantageousto lower the confidence level of the path-based location estimate tomatch the increasing probability that the operator will leave thenavigation path as the instrument advances further into the luminalnetwork.

VI. B. 2. II. Path-Based Navigation—Outside of Mapped Portion of LuminalNetwork

In addition to the use of the path in estimating the location of theinstrument when within the mapped portion of the luminal network, thepath may also be used as a data source in when the instrument is locatedoutside of the mapped portion of the luminal network. In particular, themodel of a given luminal network may not fully map the entirety of theluminal network. FIG. 13 is an example view of a model 1300 (e.g., 3Dmodel data stored in the 3D model data store 940) overlaid on a luminalnetwork 1310 in accordance with aspects of this disclosure. In someinstances, limitations in the imaging and mapping techniques used togenerate the model 1300 may prevent generation of a model thatcorresponds to the entire luminal network 1310. For example, certainbranched lumens within the luminal network may be sufficiently smallthat they cannot be clearly depicted and analyzed with common imagingand mapping techniques. As such, the model 1300 may not provide acomplete representation of the luminal network 1310, for example,leaving various portions of the luminal network 1310 unmapped and/orunrepresented in the model 1300.

For example, as shown in FIG. 13, the model 1300 can correspond to amapped portion 1320 of the luminal network 1310. An unmapped portion1330 of the luminal network 1310, which may not be represented by themodel 1300, may extend beyond the mapped portion 1320. A portion 1350 ofthe model 1300 including a section of the mapped portion 1320 of theluminal network 1310 and a section of the unmapped portion 1330 of theluminal network 1310 is enlarged in FIG. 15, which is described below.

Certain algorithms for estimating the location of the instrument mayutilize the 3D model data received from the 3D model data store in orderto generate estimated state data representative of the location of theinstrument. For example, each of the EM-based algorithm module 950, theimage-based algorithm module 960, and the robot-based algorithm module970 may use 3D model data received from the 3D model data store 940 inestimating the state data. Accordingly, if the instrument is driven toan unmapped portion (e.g., unmapped portion 1330 of FIG. 13) of aluminal network, the 3D model data store 940 may not have 3D model datawhich can be used in the estimation of the location of the instrument.Thus, aspects of this disclosure relate to the use of the path data(e.g., stored in the path data store 945) to address the lack of 3Dmodel data used to estimate the instrument location.

FIG. 14 is a flowchart illustrating another example method operable by arobotic system, or component(s) thereof, for using path-based data innavigation outside of unsegmented portions of tubular networks inaccordance with aspects of this disclosure. For example, the steps ofmethod 1400 illustrated in FIG. 14 may be performed by processor(s)and/or other component(s) of a medical robotic system (e.g., surgicalrobotic system 500) or associated system(s) (e.g., the path-basedalgorithm module 945 of the navigation configuration system 900). Forconvenience, the method 1400 is described as performed by the navigationconfiguration system, also referred to simply as the “system” inconnection with the description of the method 1400.

The method 1400 begins at block 1401. At block 1405, the system maydetermine that the path leaves a mapped portion of a luminal network ofa patient before reaching a target. In performing the method 1400, thesystem may include at least one computer-readable memory having storedthereon a model of the mapped portion of the luminal, a position of thetarget with respect to the model, and the path along at least a portionof the model from an access point to the target. Thus, block 1405 mayinvolve the processor determining that at least a portion of the pathextends through an unmapped portion of the luminal network to thetarget.

In related aspects, further details relating to block 1405 are describedbelow with reference to FIG. 15. In particular, the description below inconnection with FIG. 15 provides further detail regarding a number ofembodiments detailing how the system may determine that the path hasleft the mapped portion of the luminal network.

At block 1410, the system may display a current location of aninstrument via at least a first modality (e.g., the navigation module905 of FIG. 9A). The first modality may include the system deriving alocation of the instrument based on location data received from a set ofone or more location sensors and the mapped portion of the model.Examples of the location data include image data, EM data, and robotdata. Depending on the embodiment, the first modality may include anestimate of the location of the instrument via one or more of theEM-based algorithm module 950, the image-based algorithm module 960, andthe robot-based algorithm module 970.

At block 1415, the system may determine, based on the current locationof the instrument, that the distal end of the instrument is within athreshold range of a point at which the path leaves the mapped portionof the luminal network.

In one embodiment, the system may determine that the instrument iswithin a threshold range of a point at which the path leaves the mappedportion of the luminal network based on determining that the instrumentis located within the second to last segment 1525 as illustrated in theexample of FIG. 15. The system may identify the second to last segment1525 as a segment of the model 1300 which is adjacent to the lastsegment 1520 and located along the path 1505.

In another embodiment, the system may determine that the distal end ofthe instrument is within a threshold range of a point at which the pathleaves the mapped portion of the luminal network based on identifyingthe location(s) of one of more unmapped intersections between the lastsegment 1520 and one or more unmapped segments 1530 of the luminalnetwork 1310. In one implementation, the system may use images capturedusing a camera located at or near the distal end of the instrument. Thesystem may be configured to identify visual objects within the imagethat are representative of an intersection (e.g., a bifurcation) in theluminal network 1310. The system may use the detected visual objects toestimate the location of the intersection between the last segment 1520and the unmapped segment(s) 1530.

The system may also determine the distal end of the instrument is withina threshold range of a point at which the path leaves the mapped portionof the luminal network based on the distance between the currentlocation of the instrument and the location of the intersection. Incertain embodiments, the system may determine that the current locationof the instrument is within a defined distance from the location of theone of more unmapped intersections.

At block 1420, the system may, in response to determining that that thedistal end of the instrument is within the threshold range of the point,update the current location of the instrument based on a reduction of aweight given to the first modality. In addition to or in place ofreducing the weight given to the first modality based on the thresholdrange, the system may also use one or more other conditions for reducingthe weight given to the first modality. For example, one such conditionmay include determining that the instrument is located within the secondto last segment (e.g., the second to last segment 1525 of FIG. 15).Another condition may include determining that the instrument is withina threshold distance from the second to last segment 1525. In anotheraspect, the condition may include determining that the current locationof the instrument is within a defined distance from the location of oneof more unmapped intersections present in the last segment 1520.

The system may also be configured to increase the weight given to thefirst modality in response to the instrument returning to the mappedportion of the luminal network. This increase in the weight given to thefirst modality may include returning the weight to the original valueprior to reducing the weight in block 1420.

In certain implementations, the system may determine, based on thecurrent location of the instrument, that the distal end of theinstrument has returned to the mapped portion of the luminal networkfrom outside of the mapped portion of the luminal network (e.g., fromthe unmapped portion of the luminal network). In response to determiningthat the distal end of the instrument has returned to the mapped portionof the luminal network, the system may update the current location ofthe instrument based on an increase in the weight given to the firstmodality. That is, the system may return to the use of the 3D model datafrom the 3D model data store 940 in one or more of the EM-basedalgorithm module 950, the image-based algorithm module 960, and therobot-based algorithm module 970.

The determination that the instrument has returned to the mapped portionof the luminal network may also involve the system storing an indicationof the location at which the instrument left the mapped portion of theluminal network. For example, the system may determine a location of theinstrument at which the estimation of location of the instrument wasfirst based on the reduced weight given to the first modality. Inresponse to determining that the instrument is retracted to theabove-mentioned location, the system may then determine that theinstrument has been retracted to within the mapped portion of theluminal network.

In some implementations, the reduction of the weight given to the firstmodality may include entering a path tracing mode. The path tracing modemay include, for example, the system displaying, on a user display,visual indicia indicative of previous locations of the instrument withrespect to the model. The path tracing mode may also be referred to as a“breadcrumb” mode where new visual indicia are displayed on the userdisplay at regular intervals. In certain implementations, the visualindicia may be indicative of historical positions of the instrumentwithin the luminal network, and particularly, within the unmappedportion of the luminal network. Depending on the embodiment, the systemmay determine the location of the instrument without reference to atleast one of the image data, the EM data, and the robot data when in thepath tracing mode. Certain embodiments which may be used to calculateand apply an offset to EM data are described in U.S. Application No.62/572,285, filed on Oct. 13, 2017, and U.S. patent application Ser. No.16/143,362, filed on Sep. 26, 2018, each of which is incorporated hereinby reference in its entirety.

In other implementations, the reduction of the weight given to the firstmodality may include entering a second modality for determining thelocation of the distal end of the instrument. Referring back to FIG. 9A,in the second modality the system 900 may determine the outputnavigation data provided to the output navigation data store 990 usingthe outside segmentation navigation module 907 in place of thenavigation module 905. The outside segmentation navigation module 907may locate (or determine) the estimated state of the medical instrumentwithin a tubular network base on input data received from at least oneof the EM data store 920, the robot data store 930, the 3D model datastore 940, and the path data store 945. As described above, the system900 may determine to enter the second modality based on path datareceived from the path data store 945.

The system may determine the location of the instrument in the secondmodality via, for example, deriving the location of the instrument basedon location data (e.g., EM data, and robot data) independent of themapped portion of the model (e.g., 3D model data). In particular, theoutside segmentation navigation module 907 may use data received fromeach of the EM data store 920, the robot data store 930, and the 3Dmodel data store 940 to determine a registration for the EM data.Additionally, outside segmentation navigation module 907 may use therobot data to track the amount of insertion and/or retraction of theinstrument. The outside segmentation navigation module 907 may useinsertion and/or retraction data to determine whether the instrument hasbeen retracted to the point at which the second modality was entered andswitch back to the first modality based on the instrument beingretracted to this point.

The outside segmentation navigation module 907 may also use the 3D modeldata to apply an offset to the registered EM data when transitioningfrom the use of the navigation module 905 to the outside segmentationnavigation module 907. The offset may prevent a sudden jump in theoutput navigation data which may otherwise occur during the transition.Certain embodiments which may be used to calculate and apply an offsetto EM data are described in U.S. Application No. 62/607,246, filed onDec. 18, 2017, and U.S. patent application Ser. No. 16/221,020, filed onDec. 14, 2018, each of which is incorporated herein by reference in itsentirety. In certain implementations, the outside segmentationnavigation module 907 may produce output navigation data usingregistered EM data. Thus, the outside segmentation navigation module 907may first determine the registration for the EM data prior todetermining the output navigation data. In some implementations, thesystem 900 may determine the registration based on the instrument beingdriven a predetermined distance into the luminal network (e.g., into athird-generation segment). Thus, the outside segmentation navigationmodule 907 may begin producing the output navigation data in response tothe instrument being driven into a third-generation segment (e.g., thethird generation segments 1241, 1243, 1245, and 1247 of FIG. 12). Themethod 1400 ends at block 1425.

A number of example embodiments which may be used to determine whetherthe path leaves the mapped portion of the luminal network will bediscussed in connection with FIG. 15. FIG. 15 illustrates a portion 1350of the luminal network 1310 of FIG. 13 including a mapped portion 1320and an unmapped portion 1330 in accordance with aspects of thisdisclosure. As shown in the example of FIG. 15, a target 1510 may belocated within the unmapped portion 1320 of the luminal network 1310.Accordingly, a path 1505 may extend from the mapped portion 1320 of theluminal network 1310 into the unmapped portion 1320 of the luminalnetwork 1310 before reaching the target 1510. Since the unmapped portion1320 of the luminal network 1310 may not be available for the operatorto view when selecting the path 1510 (e.g., during pre-operative pathplanning as discussed in connection with FIG. 17 below), the path 1505may not necessarily follow the lumens in the unmapped portion 1320 ofthe luminal network 1310. In some embodiments, the path 1505 may followa substantially straight line between a final segment 1520 of the path1505 and the target 1510.

As shown in FIG. 15, the model 1300 includes a plurality of segments,including a first segment 1520 and a second segment 1525. The firstsegment 1520 may represent the final segment 1520 of the model 1300before the path 1505 leaves the mapped portion 1320 of the luminalnetwork 1310 while the second segment 1525 may represent the second tolast (also referred to as the “penultimate”) segment 1525 of the model1300 before the path 1505 leaves the mapped portion 1320 of the luminalnetwork 1310. The system may determine that the path leaves the mappedportion 1320 of the luminal network, for example at block 1405illustrated in FIG. 14, based on the identification of the final segment1520 and/or the second to last segment 1525.

In certain embodiments, the system may determine a point at which thepath 1505 leaves the mapped portion 1320 of the luminal network 1310based on an identification of the last and/or second to last segments1520 and/or 1525. Thus, the system may determine that the path 1505leaves the mapped portion 1320 of the luminal network 1310 beforereaching the target 1510 based on a determination that the path 1505leaves the mapped portion 1320 of the luminal network 1310 from the lastsegment 1520 of the model 1300.

In another embodiment, the system may determine that the instrumentleaves the mapped portion 1320 of the luminal network 1310 beforereaching the target 1510 based on a determination that the instrument iswithin a threshold distance from the second segment 1525. Thus, thesystem may determine the distance between the current location of theinstrument and the point at which the path leaves the mapped portion1320 of the luminal network 1310 and compare the distance to thethreshold distance. In one embodiment, the system may determine thedistance between the current location of the instrument and the point atwhich the path 1505 leaves the mapped portion 1320 of the luminalnetwork 1310 as the length of the path 1505 between current location ofthe instrument and the point at which the path 1505 leaves the mappedportion 1320 of the luminal network 1310. In other embodiment, thesystem may determine the Euclidean distance between current location ofthe instrument and the point at which the path 1505 leaves the mappedportion 1320 of the luminal network 1310.

FIG. 16 is a view of a 3D model including tracked locations of a distalend of an instrument in accordance with aspects of this disclosure. Inthe example of FIG. 16, the view includes a 3D model of a luminalnetwork 1600, a first set of tracked estimated locations 1605 of theinstrument and a second set of tracked estimated locations 1610 of theinstrument. The mapped and unmapped portions of the luminal network 1600are not illustrated in FIG. 16.

The first set of tracked estimated locations 1605 represent theestimated location of the distal end of the instrument as estimated bythe first modality as described in connection with FIG. 14 above,without any change to the weight given to the first modality. Incontrast, the second set of tracked estimated locations 1610 representthe estimated location of the distal end of the instrument as estimatedby the first modality as described in connection with FIG. 14 above,including the reduction to the weight given to the first modalityperformed in block 1420.

In the example of FIG. 16, the instrument was driven outside of a mappedportion of the luminal network 1600. Since the first modality was usedwithout change to the weight given thereto, the first set of trackedestimated locations 1605 continued to use the preoperative 3D model datafrom the 3D model data store 940 even after the instrument left themapped portion of the luminal network 1600. Accordingly, the first setof tracked estimated locations 1605 are not closely matched to thelocation of the unmapped portion of the luminal network 1600 and thus,provide an inaccurate estimate of the instrument location. The secondset of tracked estimated locations 1610 illustrate an embodiment wherethe weight given to the first modality is reduced and may includeentering a path tracing mode when the instrument is located in thesecond to last segment of the mapped portion of the model. Here, thesecond set of tracked estimated locations 1610 more closely track theactual locations of the luminal network 1600 than the first set oftracked estimated locations 1605 due to the switch from the firstmodality to the second modality.

VII. Pre-Operative Path Planning for Navigation Preparation

Navigating to a particular point in a tubular network of a patient'sbody may involve taking certain steps pre-operatively to generate theinformation used to create the 3D model of the tubular network and todetermine a navigation path. FIG. 17 shows an example pre-operativemethod for preparation of a surgical instrument (e.g., an instrumenttip) to navigate through an example tubular network, according tovarious embodiments. In particular, FIG. 17 shows an examplepre-operative method 1700 for navigating the instrument tip to aparticular site within the tubular network. Alongside each step of themethod 1700, a corresponding image is shown to illustrate arepresentation of the involved data for planning a path and navigatingthrough the tubular network.

Initially, at block 1705, a scan/image generated based on preoperativemodel data (e.g., CT scan) of the tubular network is obtained, and thedata from the CT scan provides 3D information about the structure andconnectivity of the tubular network. For example, the image at block1705 shows a tomographic slice of a patient's lungs.

At block 1710, a 3D model is generated based on the obtained CT scandata, and the generated 3D model can be used to assign each branch ofthe tubular network with a unique identity, enabling convenientnavigation within the network. For example, the image at block 1710shows a 3D model of a patient's bronchial network.

At block 1715, a target 1716 is selected, and this target may be, forexample, a lesion to biopsy, or a portion of organ tissue to repairsurgically. In one embodiment, the system provides a user capability forselecting the location of the target by interfacing with a computerdisplay that can show the 3D model, such as by clicking with a mouse ortouching a touchscreen. The selected target may then be displayed to theuser. For example, the target 1716 is marked within the 3D bronchialmodel generated from step 1710.

At block 1720, a path 1721 is automatically planned from an entry point1722 to the target 1716, and the path 1721 identifies a sequence ofbranches within the network to travel through, so as to reach the target1716. In one embodiment, the tubular network may be tree-like, the path1721 may be uniquely determined by the structure of the tubular network,while in another embodiment, the tubular network may be cyclic, and thepath may be found by an appropriate algorithm such as a shortest-pathalgorithm.

Once the path 1721 has been determined, virtual endoscopy 1725 may beperformed to give the user a preview of the endoscopic procedure. The 3Dmodel generated from step 1710 is used to generate a sequence of 2Dimages as though seen by an endoscope tip while navigating the networkcorresponding to the 3D model. The path 1721 may be shown as a curvethat may be followed to get from the entry point 1722 to the target1716.

Once the virtual endoscope tip has arrived at the target 1716, a virtualtool alignment procedure 1730 may be performed to illustrate to the userhow to manipulate endoscopic tools in order to perform a surgicalprocedure at the target. For example, in the illustration, a virtualendoscopic biopsy needle 1731 is maneuvered by the user in order tobiopsy a lesion 1732 located beneath the surface of a bronchial tube.The lesion location is highlighted so that the user can align the needleto it, and then use the needle to pierce the surface of the bronchialtube and access the lesion underneath. This mimics the steps that willbe taken during the actual surgical procedure, allowing the user topractice before performing surgery.

VIII. Implementing Systems and Terminology

Implementations disclosed herein provide systems, methods andapparatuses for path-based navigation of tubular networks.

It should be noted that the terms “couple,” “coupling,” “coupled” orother variations of the word couple as used herein may indicate eitheran indirect connection or a direct connection. For example, if a firstcomponent is “coupled” to a second component, the first component may beeither indirectly connected to the second component via anothercomponent or directly connected to the second component.

The path-based navigational functions described herein may be stored asone or more instructions on a processor-readable or computer-readablemedium. The term “computer-readable medium” refers to any availablemedium that can be accessed by a computer or processor. By way ofexample, and not limitation, such a medium may comprise random accessmemory (RAM), read-only memory (ROM), electrically erasable programmableread-only memory (EEPROM), flash memory, compact disc read-only memory(CD-ROM) or other optical disk storage may comprise RAM, ROM, EEPROM,flash memory, CD-ROM or other optical disk storage, magnetic diskstorage or other magnetic storage devices, or any other medium that canbe used to store desired program code in the form of instructions ordata structures and that can be accessed by a computer. It should benoted that a computer-readable medium may be tangible andnon-transitory. As used herein, the term “code” may refer to software,instructions, code or data that is/are executable by a computing deviceor processor.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isrequired for proper operation of the method that is being described, theorder and/or use of specific steps and/or actions may be modifiedwithout departing from the scope of the claims.

As used herein, the term “plurality” denotes two or more. For example, aplurality of components indicates two or more components. The term“determining” encompasses a wide variety of actions and, therefore,“determining” can include calculating, computing, processing, deriving,investigating, looking up (e.g., looking up in a table, a database oranother data structure), ascertaining and the like. Also, “determining”can include receiving (e.g., receiving information), accessing (e.g.,accessing data in a memory) and the like. Also, “determining” caninclude resolving, selecting, choosing, establishing and the like.

The phrase “based on” does not mean “based only on,” unless expresslyspecified otherwise. In other words, the phrase “based on” describesboth “based only on” and “based at least on.”

The previous description of the disclosed implementations is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these implementations will bereadily apparent to those skilled in the art, and the generic principlesdefined herein may be applied to other implementations without departingfrom the scope of the invention. For example, it will be appreciatedthat one of ordinary skill in the art will be able to employ a numbercorresponding alternative and equivalent structural details, such asequivalent ways of fastening, mounting, coupling, or engaging toolcomponents, equivalent mechanisms for producing particular actuationmotions, and equivalent mechanisms for delivering electrical energy.Thus, the present invention is not intended to be limited to theimplementations shown herein but is to be accorded the widest scopeconsistent with the principles and novel features disclosed herein.

What is claimed is:
 1. A medical robotic system, comprising: aninstrument configured to be driven through a luminal network; a roboticarm attached to the instrument and configured to move the instrument; aset of one or more processors; and at least one computer-readable memoryin communication with the set of processors and having stored thereon amodel of the luminal network of a patient, a position of a target withrespect to the model, and a path along at least a portion of the modelfrom an access point to the target, the memory further having storedthereon computer-executable instructions to cause the set of processorsto: command the robotic arm to move the instrument to a location withinthe luminal network, receive location data from at least one of a set oflocation sensors and the robotic arm command, the location data beingindicative of the location of the instrument, determine a first estimateof the location of the instrument at a first time based on the locationdata, obtain a distance that the instrument has travelled into theluminal network from the access point based on the robotic arm command,determine a second estimate of the location of the instrument at thefirst time based on the path and the distance, determine a weight forthe second estimate based on the distance, and determine the location ofthe instrument at the first time based on the first estimate, the secondestimate, and the weight.
 2. The system of claim 1, wherein: the modelcomprises a plurality of segments, each segment associated with ageneration count determined based on a number of branches in the luminalnetwork located between the segment and the access point, the memoryfurther has stored thereon computer-executable instructions to cause theset of processors to: identify a first one of the segments in which theinstrument is located, and determine the weight for the second estimatebased on the generation count associated with the first segment.
 3. Thesystem of claim 2, wherein the weight for the second estimate decreasesin value as the generation count increases.
 4. The system of claim 2,wherein the memory further has stored thereon computer-executableinstructions to cause the set of processors to: determine that thegeneration count associated with the first segment is greater than athreshold generation count, and set the weight to zero in response todetermining that the generation count associated with the first segmentis greater than the threshold generation count.
 5. The system of claim1, wherein: the model comprises a plurality of segments, the memoryfurther has stored thereon computer-executable instructions to cause theset of processors to: identify a first one of the segments in which theinstrument is located, determine a diameter of the first segment, anddetermine the weight for the second estimate based on the diameter ofthe first segment.
 6. The system of claim 1, wherein: the set oflocation sensors comprise: a camera located at a distal end of theinstrument and configured to generate image data, and a set of one ormore electromagnetic (EM) sensors located at the distal end of theinstrument and configured to generate EM data, the memory further hasstored thereon computer-executable instructions to cause the set ofprocessors to determine robot data indicative of the location of theinstrument based on the robot command inputs, and the determining of thefirst estimate of the location of the instrument is further based on theimage data and the EM data.
 7. A non-transitory computer readablestorage medium having stored thereon instructions that, when executed,cause at least one computing device to: command a robotic arm to move aninstrument to a location within a luminal network of a patient, receivelocation data from at least one of a set of location sensors and therobotic arm command, the location data being indicative of the locationof the instrument; determine a first estimate of the location of theinstrument at a first time based on the location data; obtain a distancethat the instrument has travelled into the luminal network from anaccess point based on the robotic arm command; determine a secondestimate of the location of the instrument at the first time based on apath stored on at least one computer-readable memory and the distance,the non-transitory computer readable storage medium further havingstored thereon a model of the luminal network, a position of a targetwith respect to the model, and the path, the path defined along at leasta portion of the model from the access point to the target, determine aweight for the second estimate based on the distance, and determine thelocation of the instrument at the first time based on the firstestimate, the second estimate, and the weight.
 8. The non-transitorycomputer readable storage medium of claim 7, wherein: the modelcomprises a plurality of segments, each segment associated with ageneration count determined based on a number of branches in the luminalnetwork located between the segment and the access point, thenon-transitory computer readable storage medium further has storedthereon instructions that, when executed, cause the at least onecomputing device to: identify a first one of the segments in which theinstrument is located; and determine the weight for the second estimatebased on the generation count associated with the first segment.
 9. Thenon-transitory computer readable storage medium of claim 8, wherein theweight for the second estimate decreases in value as the generationcount increases.
 10. The non-transitory computer readable storage mediumof claim 8, further having stored thereon instructions that, whenexecuted, cause the at least one computing device to: determine that thegeneration count associated with the first segment is greater than athreshold generation count, and set the weight to zero in response todetermining that the generation count associated with the first segmentis greater than the threshold generation count.
 11. The non-transitorycomputer readable storage medium of claim 7, wherein: the modelcomprises a plurality of segments, the non-transitory computer readablestorage medium further has stored thereon instructions that, whenexecuted, cause the at least one computing device to: identify a firstone of the segments in which the instrument is located, determine adiameter of the first segment, and determine the weight for the secondestimate based on the diameter of the first segment.
 12. Thenon-transitory computer readable storage medium of claim 7, wherein: theset of location sensors comprise: a camera located at a distal end ofthe instrument and configured to generate image data, and a set of oneor more electromagnetic (EM) sensors located at the distal end of theinstrument and configured to generate EM data, the non-transitorycomputer readable storage medium further has stored thereon instructionsthat, when executed, cause the at least one computing device todetermine robot data indicative of the location of the instrument basedon the robot command inputs, and the determining of the first estimateof the location of the instrument is further based on the image data andthe EM data.
 13. A method of estimating a location of an instrument,comprising: commanding a robotic arm to move an instrument to a locationwithin a luminal network of a patient, receiving location data from atleast one of a set of location sensors and the robotic arm command, thelocation data being indicative of the location of the instrument;determining a first estimate of the location of the instrument at afirst time based on the location data; obtaining a distance that theinstrument has travelled into the luminal network from an access pointbased on the robotic arm command; determining a second estimate of thelocation of the instrument at the first time based on a path stored onat least one computer-readable memory and the distance, at least onecomputer-readable memory having stored thereon a model of the luminalnetwork, a position of a target with respect to the model, and the path,the path defined along at least a portion of the model from the accesspoint to the target, determine a weight for the second estimate based onthe distance, and determining the location of the instrument at thefirst time based on the first estimate, the second estimate, and theweight.
 14. The method of claim 13, wherein: the model comprises aplurality of segments, each segment associated with a generation countdetermined based on a number of branches in the luminal network locatedbetween the segment and the access point, the method further comprises:identifying a first one of the segments in which the instrument islocated; and determining the weight for the second estimate based on thegeneration count associated with the first segment.
 15. The method ofclaim 14, wherein the weight for the second estimate decreases in valueas the generation count increases.
 16. The method of claim 14, furthercomprising: determining that the generation count associated with thefirst segment is greater than a threshold generation count, and settingthe weight to zero in response to determining that the generation countassociated with the first segment is greater than the thresholdgeneration count.
 17. The method of claim 13, wherein: the modelcomprises a plurality of segments, the method further comprises:identifying a first one of the segments in which the instrument islocated; determining a diameter of the first segment; and determiningthe weight for the second estimate based on the diameter of the firstsegment.
 18. The method of claim 13, wherein: the set of locationsensors comprise: a camera located at a distal end of the instrument andconfigured to generate image data, and a set of one or moreelectromagnetic (EM) sensors located at the distal end of the instrumentand configured to generate EM data, the method further comprisesdetermining robot data indicative of the location of the instrumentbased on the robot command inputs, and the determining of the firstestimate of the location of the instrument is further based on the imagedata and the EM data.